A roadmap to computational social neuroscience

Abstract

To complement experimental efforts toward understanding human social interactions at both neural and behavioral levels, two computational approaches are presented: (1) a fully parameterizable mathematical model of a social partner, the Human Dynamic Clamp which, by virtue of experimentally controlled interactions between Virtual Partners and real people, allows for emergent behaviors to be studied; and (2) a multiscale neurocomputational model of social coordination that enables exploration of social self-organization at all levels—from neuronal patterns to people interacting with each other. These complementary frameworks and the cross product of their analysis aim at understanding the fundamental principles governing social behavior.

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Acknowledgments

This work was supported by NIMH award MH080838, NIBIB award EB025819-01, the Chaire d’Excellence Pierre de Fermat, the FAU Foundation and the Davimos Family Endowment for Excellence in Science. The authors acknowledge helpful comments by reviewers and editor.

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Correspondence to Emmanuelle Tognoli.

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Tognoli, E., Dumas, G. & Kelso, J.A.S. A roadmap to computational social neuroscience. Cogn Neurodyn 12, 135–140 (2018). https://doi.org/10.1007/s11571-017-9462-0

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Keywords

  • Social coordination
  • HKB
  • Spatiotemporal patterns
  • Coordination dynamics